Molecular diversity among wild relatives of Cajanus cajan (L.) Millsp.

The wild relatives of pigeonpea [Cajanus cajan (L.) Millsp.] are important source of genetic variation carrying genes for resistance to various biotic and abiotic stresses and other morphological traits. In the present study, four wild relatives of pigeonpea were evaluated using 24 simple sequence repeat (SSR) markers to assess their genetic diversity at molecular level. Each marker, on average, amplified 3.3 alleles with polymorphic information content (PIC) value of 0.53. The dendrogram pattern revealed two distinct genotypic clusters and cultivated pigeonpea was closely related to Cajanus cajanifolius. On the contrary, Cajanus scarabaeoides was the most diverse from the cultivated type. The results also suggest that genetic distance between cultivated pigeonpea and wild species was not related to their hybridization barrier.Keywords: Cajanus, crossability, genetic diversity, simple sequence repeat markers, wild relativesAfrican Journal of Biotechnology Vol. 12(24), pp. 3797-380

Sequencing Pigeonpea Genome

Availability of draft genome has brought quantum jump in pigeonpea status and facilitated to move it to the league of genomic resource rich crops. It is important to mention that pigeonpea became the first orphan and non-industrial grain legume in 2012 to have the draft genome sequence. An elite pigeonpea genotype Asha (ICPL 87119) was used to develop the draft genome in two different sequencing efforts. The pigeonpea genome sequence effort led by International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) used Illumina Genome Analyzer and HiSeq 2000 NGS platform, and a total of 237.2 Gb of sequence was generated. De-novo genome assembly combined with Sanger-based bacterial artificial chromosome end sequences and a genetic map was used to assemble raw reads into scaffolds representing 72.7% (605.78 Mb) of the 833.07 Mb pigeonpea genome. Genome analysis predicted 48,680 genes with an average transcript length of 2348 bp, coding-sequence size of 959.35 bp and 3.59 exons per gene. Analysis of genome assembly for repetitive DNA showed presence of transposable elements (TEs) in 49.61% of assembled genome. The pigeonpea genome sequencing led by National Research Centre on Plant Biotechnology (NRCPB) used 454 GS-FLX sequencing chemistry, with mean read lengths of >550 bp and >10-fold genome coverage, was used to assemble ~511 Mb sequence data. In this study, 47,004 protein-coding genes were predicted. This study also reported 1213 disease resistance/defense response genes and 152 abiotic stress tolerance genes. The available pigeonpea draft genome information is expected to facilitate genomics-assisted breeding for the targeted traits that could improve food security in many developing countries

Whole-Genome Sequencing of Pigeonpea: Requirement, Background History, Current Status and Future Prospects for Crop Improvement

Despite of being a very important crop, pigeonpea did not have genomic resources until 2005. Pigeonpea Genomics Initiative (PGI) supported by Indian Council of Agricultural Research (ICAR) under Indo-US Agriculture Knowledge Initiative was the first major initiative that delivered first set of molecular markers in large numbers, first set of mapping populations, first set of transcriptome assemblies, etc. Subsequently, two consortia—1) International Initiative for Pigeonpea Genomics (IIPG), led by International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and 2) Led by National Research Centre on Plant Biotechnology (NRCPB)—delivered two draft genome assemblies for Asha (ICPL 87119) variety. In summary, all these genomic resources transformed pigeonpea from an ‘orphan crop’ to ‘genomics resources-rich crop’. After publication of draft genome sequences, a detailed plan was developed to utilize draft genome information for pigeonpea improvement. This plan in the form of a proposal was approved by Ministry of Agriculture, Government of India and United States Agency for International Development (USAID)—India. In addition to this major project, two additional projects were funded by Department of Biotechnology, Government of India. All these efforts have established high-density genotyping platforms such as genotyping by sequencing (GBS) and ​‘Axiom® CajanusSNP Array’, produced the first generation HapMap, generated whole-genome re-sequencing data of >400 pigeonpea lines, evaluated several mapping populations for desired traits, established marker–trait association for several traits of interest to breeders and also identified best-performing lines. Additionally, multi-parent advance generation inter-cross (MAGIC) and nested association mapping (NAM) populations are being developed. With the availability of above-mentioned information, next few years will be witnessing application of genomics-assisted breeding for pigeonpea improvement. It is anticipated that improved pigeonpea lines developed through genomics interventions will reach to farmers’ fields and elevate the game towards pulse sufficiency for poor farmers in arid and semi-arid regions of the world in near future

Genetic diversity in Indian isolates of Fusarium oxysporum f. sp. ciceris, chickpea wilt pathogen

Forty-eight isolates of FOC collected from different chickpea growing regions in India were evaluated for genetic variations using amplified fragment length polymorphism (AFLP). Out of 48 isolates, 41 werefound pathogenic and seven non-pathogenic. Pathogenic isolates differ in their virulence however; there was no apparent correlation between geographical origin and virulence of the isolates. The genetic variation was evaluated by the AFLP analysis. A total 339 fragments were scored following selective amplification with five EcoR1 and Mse1 primer combinations E-TC/M-CAT, E-TC/M-CAC, EAC/ M-CAG, E-TA/MCAG, E-TA/M-CAG, out of which 331 fragments were polymorphic. UPGMA cluster analysis and principle coordinate analysis distinctly classified 48 isolates into two major groups; pathogenic and non-pathogenic. The pathogenic isolates could be further clustered into six majorgroups at 0.77 genetic similarities. Region specific grouping was observed with in few isolates. The results of the present study provide evidence of the high discriminatory power of AFLP analysis,suggesting the applicability of this method to the molecular characterization of Fusarium oxysporum f.sp. ciceris

Advances in genetics and molecular breeding of three legume crops of semi-arid tropics using next-generation sequencing and high-throughput genotyping technologies

Molecular markers are the most powerful genomic tools to increase the efficiency and precision of breeding practices for crop improvement. Progress in the development of genomic resources in the leading legume crops of the semi-arid tropics (SAT), namely, chickpea (Cicer arietinum), pigeonpea (Cajanus cajan) and groundnut (Arachis hypogaea), as compared to other crop species like cereals, has been very slow. With the advances in next-generation sequencing (NGS) and high-throughput (HTP) genotyping methods, there is a shift in development of genomic resources including molecular markers in these crops. For instance, 2,000 to 3,000 novel simple sequence repeats (SSR) markers have been developed each for chickpea, pigeonpea and groundnut. Based on Sanger, 454/FLX and Illumina transcript reads, transcriptome assemblies have been developed for chickpea (44,845 transcript assembly contigs, or TACs) and pigeonpea (21,434 TACs). Illumina sequencing of some parental genotypes of mapping populations has resulted in the development of 120 million reads for chickpea and 128.9 million reads for pigeonpea. Alignment of these Illumina reads with respective transcriptome assemblies have provided >10,000 SNPs each in chickpea and pigeonpea. A variety of SNP genotyping platforms including GoldenGate, VeraCode and Competitive Allele Specific PCR (KASPar) assays have been developed in chickpea and pigeonpea. By using above resources, the first-generation or comprehensive genetic maps have been developed in the three legume speciesmentioned above. Analysis of phenotyping data together with genotyping data has provided candidate markers for drought-tolerance-related root traits in chickpea, resistance to foliar diseases in groundnut and sterility mosaic disease (SMD) and fertility restoration in pigeonpea. Together with these traitassociated markers along with those already available, molecular breeding programmes have been initiated for enhancing drought tolerance, resistance to fusarium wilt and ascochyta blight in chickpea and resistance to foliar diseases in groundnut. These trait-associated robust markers along with other genomic resources including genetic maps and genomic resources will certainly accelerate crop improvement programmes in the SAT legum

Groundnut Entered Post-genome Sequencing Era: Opportunities and Challenges in Translating Genomic Information from Genome to Field

Cultivated groundnut or peanut (Arachis hypogaea) is an allopolyploid crop with a large complex genome and genetic barrier for exchanging genetic diversity from its wild relatives due to ploidy differences. Optimum genetic and genomic resources are key for accelerating the process for trait mapping and gene discovery and deploying diagnostic markers in genomics-assisted breeding. The better utilization of different aspects of peanut biology such as genetics, genomics, transcriptomics, proteomics, epigenomics, metabolomics, and interactomics can be of great help to groundnut genetic improvement program across the globe. The availability of high-quality reference genome is core to all the “omics” approaches, and hence optimum genomic resources are a must for fully exploiting the potential of modern science into conventional breeding. In this context, groundnut is passing through a very critical and transformational phase by making available the required genetic and genomic resources such as reference genomes of progenitors, resequencing of diverse lines, transcriptome resources, germplasm diversity panel, and multi-parent genetic populations for conducting high-resolution trait mapping, identification of associated markers, and development of diagnostic markers for selected traits. Lastly, the available resources have been deployed in translating genomic information from genome to field by developing improved groundnut lines with enhanced resistance to root-knot nematode, rust, and late leaf spot and high oleic acid. In addition, the International Peanut Genome Initiative (IPGI) have made available the high-quality reference genome for cultivated tetraploid groundnut which will facilitate better utilization of genetic resources in groundnut improvement. In parallel, the development of high-density genotyping platforms, such as Axiom_Arachis array with 58 K SNPs, and constitution of training population will initiate the deployment of the modern breeding approach, genomic selection, for achieving higher genetic gains in less time with more precision

Development and use of genic molecular markers (GMMs) for construction of a transcript map of chickpea (Cicer arietinum L.)

A transcript map has been constructed by the development and integration of genic molecular markers (GMMs) including single nucleotide polymorphism (SNP), genic microsatellite or simple sequence repeat (SSR) and intron spanning region (ISR)-based markers, on an inter-specific mapping population of chickpea, the third food legume crop of the world and the first food legume crop of India. For SNP discovery through allele re-sequencing, primer pairs were designed for 688 genes/expressed sequence tags (ESTs) of chickpea and 657 genes/ESTs of closely related species of chickpea. High-quality sequence data obtained for 220 candidate genic regions on 2–20 genotypes representing 9 Cicer species provided 1,893 SNPs with an average frequency of 1/35.83 bp and 0.34 PIC (polymorphism information content) value. On an average 2.9 haplotypes were present in 220 candidate genic regions with an average haplotype diversity of 0.6326. SNP2CAPS analysis of 220 sequence alignments, as mentioned above, provided a total of 192 CAPS candidates. Experimental analysis of these 192 CAPS candidates together with 87 CAPS candidates identified earlier through in silico mining of ESTs provided scorable amplification in 173 (62.01%) cases of which predicted assays were validated in 143 (82.66%) cases (CGMM). Alignments of chickpea unigenes with Medicago truncatula genome were used to develop 121 intron spanning region (CISR) markers of which 87 yielded scorable products. In addition, optimization of 77 EST-derived SSR (ICCeM) markers provided 51 scorable markers. Screening of easily assayable 281 markers including 143 CGMMs, 87 CISRs and 51 ICCeMs on 5 parental genotypes of three mapping populations identified 104 polymorphic markers including 90 markers on the inter-specific mapping population. Sixty-two of these GMMs together with 218 earlier published markers (including 64 GMM loci) and 20 other unpublished markers could be integrated into this genetic map. A genetic map developed here, therefore, has a total of 300 loci including 126 GMM loci and spans 766.56 cM, with an average inter-marker distance of 2.55 cM. In summary, this is the first report on the development of large-scale genic markers including development of easily assayable markers and a transcript map of chickpea. These resources should be useful not only for genome analysis and genetics and breeding applications of chickpea, but also for comparative legume genomics

Single feature polymorphisms (SFPs) for drought tolerance in pigeonpea (Cajanus spp.)

Single feature polymorphisms (SFPs) are microarray-based molecular markers that are detected by hybridization of DNA or cRNA to oligonucleotide probes. With an objective to identify the potential polymorphic markers for drought tolerance in pigeonpea [Cajanus cajan (L.) Millspaugh], an important legume crop for the semi-arid tropics but deficient in genomic resources, Affymetrix Genome Arrays of soybean (Glycine max), a closely related species of pigeonpea were used on cRNA of six parental genotypes of three mapping populations of pigeonpea segregating for agronomic traits like drought tolerance and pod borer (Helicoverpa armigiera) resistance. By using robustified projection pursuit method on 15 pair-wise comparisons for the six parental genotypes, 5,692 SFPs were identified. Number of SFPs varied from 780 (ICPL 8755 × ICPL 227) to 854 (ICPL 151 × ICPL 87) per parental combination of the mapping populations. Randomly selected 179 SFPs were used for validation by Sanger sequencing and good quality sequence data were obtained for 99 genes of which 75 genes showed sequence polymorphisms. While associating the sequence polymorphisms with SFPs detected, true positives were observed for 52.6% SFPs detected. In terms of parental combinations of the mapping populations, occurrence of true positives was 34.48% for ICPL 151 × ICPL 87, 41.86% for ICPL 8755 × ICPL 227, and 81.58% for ICP 28 × ICPW 94. In addition, a set of 139 candidate genes that may be associated with drought tolerance has been identified based on gene ontology analysis of the homologous pigeonpea genes to the soybean genes that detected SFPs between the parents of the mapping populations segregating for drought tolerance

Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachishypogaea L.)

Cultivated groundnut or peanut (Arachis hypogaea L.), an allotetraploid (2n = 4x = 40), is a self pollinated and widely grown crop in the semi-arid regions of the world. Improvement of drought tolerance is an important area of research for groundnut breeding programmes. Therefore, for the identification of candidate QTLs for drought tolerance, a comprehensive and refined genetic map containing 191 SSR loci based on a single mapping population (TAG 24 × ICGV 86031), segregating for drought and surrogate traits was developed. Genotyping data and phenotyping data collected for more than ten drought related traits in 2–3 seasons were analyzed in detail for identification of main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) using QTL Cartographer, QTLNetwork and Genotype Matrix Mapping (GMM) programmes. A total of 105 M-QTLs with 3.48–33.36% phenotypic variation explained (PVE) were identified using QTL Cartographer, while only 65 M-QTLs with 1.3–15.01% PVE were identified using QTLNetwork. A total of 53 M-QTLs were such which were identified using both programmes. On the other hand, GMM identified 186 (8.54–44.72% PVE) and 63 (7.11–21.13% PVE), three and two loci interactions, whereas only 8 E-QTL interactions with 1.7–8.34% PVE were identified through QTLNetwork. Interestingly a number of co-localized QTLs controlling 2–9 traits were also identified. The identification of few major, many minor M-QTLs and QTL × QTL interactions during the present study confirmed the complex and quantitative nature of drought tolerance in groundnut. This study suggests deployment of modern approaches like marker-assisted recurrent selection or genomic selection instead of marker-assisted backcrossing approach for breeding for drought tolerance in groundnut

Plant Genetics and Molecular Biology: An Introduction

The rapidly evolving technologies can serve as a potential growth engine in agriculture as many of these technologies have revolutionized several industries in the recent past. The tremendous advancements in biotechnology methods, cost-effective sequencing technology, refinement of genomic tools, and standardization of modern genomics-assisted breeding methods hold great promise in taking the global agriculture to the next level through development of improved climate-smart seeds. These technologies can dramatically increase our capacity to understand the molecular basis of traits and utilize the available resources for accelerated development of stable high-yielding, nutritious, input-use efficient, and climate-smart crop varieties. This book aimed to document the monumental advances witnessed during the last decade in multiple fields of plant biotechnology such as genetics, structural and functional genomics, trait and gene discovery, transcriptomics, proteomics, metabolomics, epigenomics, nanotechnology, and analytical tools. This book will serve to update the scientific community, academicians, and other stakeholders in global agriculture on the rapid progress in various areas of agricultural biotechnology. This chapter provides a summary of the book, “Plant Genetics and Molecular Biology.